所以,我正在尝试使用 7 种面部表情制作情感分类器。我知道为了使用整数标签而不是 0 和 1 需要使用 sparse_categorical_crossentropy 并且需要将输出层激活作为 softmax 但它没有按预期工作。
我正在使用这里的数据集https://www.kaggle.com/ashishpatel26/facial-expression-recognitionferchallenge
import pandas as pd
import numpy as np
from PIL import Image
import random
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.layers import Conv1D, MaxPooling1D
from keras.layers import Activation, Dropout, Flatten, Dense
emotion = {0 : 'Angry', 1 : 'Disgust',2 : 'Fear',3 : 'Happy',
4 : 'Sad',5 : 'Surprise',6 : 'Neutral'}
df=pd.read_csv('fer.csv')
faces=df.values[0:500,1]
faces=faces.tolist()
emos=df.values[0:500,0]
for i in range(len(faces)):
faces[i]=[int(x) for x in faces[i].split()]
emos[i]=int(emos[i])
faces=np.array(faces)
faces=np.expand_dims(faces, axis=2)
model = Sequential()
model.add(Conv1D(16, 3, padding='same', input_shape=(2304,1), activation='relu'))
model.add(Conv1D(16, 3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(32, 3, padding='same', activation='relu'))
model.add(Conv1D(32, 3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(64, 3, padding='same', activation='relu'))
model.add(Conv1D(64, 3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(128, 3, padding='same', activation='relu'))
model.add(Conv1D(256, 3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(faces,emos,epochs=10,batch_size=8)
model.save_weights('model.h5')
W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at sparse_xent_op.cc:90 : Invalid argument: Received a label value of 6 which is outside the valid range of [0, 1). Label values: 6 0 2 4 6 0 0 3
Traceback (most recent call last):
File "FEClassifier.py", line 56, in <module>
model.fit(faces,emos,epochs=10,batch_size=8)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\keras\engine\training_arrays.py", line 199, in fit_loop
outs = f(ins_batch)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2715, in __call__
return self._call(inputs)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2675, in _call
fetched = self._callable_fn(*array_vals)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1439, in __call__
run_metadata_ptr)
File "C:\Users\nrj10\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Received a label value of 6 which is outside the valid range of [0, 1). Label values: 6 0 2 4 6 0 0 3
[[{{node loss/dense_3_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]]
如果你有 N 个类(N > 2),无论你是否使用稀疏标签,最后一层都需要有 N 个神经元:
model.add(Dense(7, activation='softmax'))
不要忘记稀疏标签只是为了方便,您的模型仍然需要为每个类生成一个分数。
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我来说两句